Comparison of Artificial Neural Networks and Autoregressive Model to Forecast Inflows to Roseires Reservoir for better Prediction of Irrigation Water Supply in the Sudan

The Blue Nile River is utilized in Sudan as the main source of irrigation water. However, the river has a long, dry, low-flow season (October–May), which necessitates the use of regulations and rules to manage its water use during this period. This depends on the use of accurate lead time forecasts of inflows to the reservoirs built along the river. Thus a reliable and tested forecasting tool is needed to provide inflow forecast, with sufficient lead time. In the present study, artificial neural network (ANN) is used to model the recession curve of the flow hydrograph at El-Deim gauging station, which subsequently is used as inflows to the Roseires Reservoir on the Blue Nile River. Different scenarios of ANN have been tested to forecast 23 10-day mean discharges during the recession period and their performances were assessed. Results from the optimal ANN model were compared to those simulated with an autoregressive (AR1) model to check their accuracy. Modelling results showed that the ANN model developed is capable of accurately forecasting the inflows to the Roseires Reservoir and outperforms the AR1 model. It has then proposed for use in operation of the reservoir for purposes of predicting irrigation water supply

Prevalence and Risk Factors of HCV Infection among Haemodialysis Patients at Dialysis Centers in Khartoum State -Sudan

Abstract: Hepatitis C virus share razors. P values = (0.0001, 0.0031, 0.0001, 0.0018, 0.0005 and 0.0002) Results: Field workers interviewed ten dialysis centers with a total of 287 study subjects. Sixty out of 287 (20.9%) was found to be anti-HC reactive. The multivariate analysis indicated as risk factors associated to anti-HCV positivity the number of blood transfusion received, duration of dialysis treatment, number of units of treatment, history of surgeries, multiple injections and usin

Comparison of Real-time PCR to ELISA for the detection of human cytomegalovirus infection in renal transplant patients in the Sudan

<p>Abstract</p> <p>Background</p> <p>This study was carried out to detect human cytomegalovirus (HCMV) IgG and IgM antibodies using an Enzyme-linked immunosorbent assay (ELISA) in renal transplant patients in Khartoum state, Sudan and to improve the diagnosis of HCMV through the introduction of Real-time Polymerase Chain Reaction (PCR) testing. A total of 98 plasma samples were collected randomly from renal transplant patients at Ibin Sina Hospital and Salma Centre for Transplantation and Haemodialysis during the period from August to September 2006.</p> <p>Results</p> <p>Among the 98 renal transplant patients, 65 were males and 33 females. The results revealed that HCMV IgG was present in all patients' plasma 98/98 (100%), while only 6/98 (6.1%) had IgM antibodies in their plasma. HCMV DNA viral loads were detected in 32 patients 32/98 (32.7%) using Real-time PCR.</p> <p>Conclusions</p> <p>The HCMV IgG results indicate a high prevalence of past HCMV infection in all tested groups, while the finding of IgM may reflect a recent infection or reactivation. HCMV detection by real-time PCR in the present study indicated a high prevalence among renal transplant patients in Khartoum. In conclusion, the prevalence of HCMV in Khartoum State was documented through detection of HCMV-specific antibodies. Further study using various diagnostic methods should be considered to determine the prevalence of HCMV disease at the national level.</p

The Association of Lymphocyte count, CRP, D-Dimer, and LDH with Severe Coronavirus Disease 2019 (COVID-19): A Meta-Analysis

Background: The rapid progression of Coronavirus disease 2019 (COVID-19) and its increasing burden on health systems necessitate the identification of parameters of severe infection to help in monitoring, prognoses and development of treatment algorithms. Objectives: This review aims to investigate the association of lymphocyte count, CRP, LDH, and D-Dimer with the severity of COVID-19. Methods: This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The databases of MEDLINE/PubMed, WHO-Virtual Health Library (VHL), and ScienceDirect were used for the systematic search. Random effects model was used to estimate the pooled standardized mean differences (SMD) with the corresponding 95% confidence interval (CI), using OpenMeta Analyst software. Results: A total of 11 studies, with 2437 COVID-19 patients, which fulfilled the eligibility criteria were included in the meta-analysis. The analysis revealed that lymphocyte count was significantly lower in patients with the severe form of COVID-19 (SMD = - 1.025, P value &lt;.001). Also, the analysis of SMD showed that patients with severe COVID-19 have a significantly higher serum levels of CRP (SMD = 3.363, P value &lt;.001), D-Dimer (SMD = 1.073, P value &lt;.001), and LDH (SMD = 3.345, P value &lt;.001). Conclusion: Low lymphocyte count and high levels of CRP, LDH, and D-Dimer are associated with severe COVID-19. These laboratory markers could be used as clinical indicators of worsening illness and poor prognosis of COVID-19

Deconstructing Conformal Blocks in 4D CFT

We show how conformal partial waves (or conformal blocks) of spinor/tensor correlators can be related to each other by means of differential operators in four dimensional conformal field theories. We explicitly construct such differential operators for all possible conformal partial waves associated to four-point functions of arbitrary traceless symmetric operators. Our method allows any conformal partial wave to be extracted from a few \u201cseed\u201d correlators, simplifying dramatically the computation needed to bootstrap tensor correlators. \ua9 2015, The Author(s)

Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan

<p>Abstract</p> <p>Background</p> <p>Large spatial, seasonal and annual variability of major drivers of the carbon cycle (precipitation, temperature, fire regime and nutrient availability) are common in the Sahel region. This causes large variability in net ecosystem exchange and in vegetation productivity, the subsistence basis for a major part of the rural population in Sahel. This study compares the 2005 dry and wet season fluxes of CO₂for a grass land/sparse savanna site in semi arid Sudan and relates these fluxes to water availability and incoming photosynthetic photon flux density (PPFD). Data from this site could complement the current sparse observation network in Africa, a continent where climatic change could significantly impact the future and which constitute a weak link in our understanding of the global carbon cycle.</p> <p>Results</p> <p>The dry season (represented by Julian day 35–46, February 2005) was characterized by low soil moisture availability, low evapotranspiration and a high vapor pressure deficit. The mean daily NEE (net ecosystem exchange, Eq. 1) was -14.7 mmol d^-1for the 12 day period (negative numbers denote sinks, i.e. flux from the atmosphere to the biosphere). The water use efficiency (WUE) was 1.6 mmol CO₂mol H₂O^-1and the light use efficiency (LUE) was 0.95 mmol CO₂mol PPFD^-1. Photosynthesis is a weak, but linear function of PPFD. The wet season (represented by Julian day 266–273, September 2005) was, compared to the dry season, characterized by slightly higher soil moisture availability, higher evapotranspiration and a slightly lower vapor pressure deficit. The mean daily NEE was -152 mmol d^-1for the 8 day period. The WUE was lower, 0.97 mmol CO₂mol H₂O^-1and the LUE was higher, 7.2 <it>μ</it>mol CO₂mmol PPFD^-1during the wet season compared to the dry season. During the wet season photosynthesis increases with PPFD to about 1600 <it>μ</it>mol m^-2s^-1and then levels off.</p> <p>Conclusion</p> <p>Based on data collected during two short periods, the studied ecosystem was a sink of carbon both during the dry and wet season 2005. The small sink during the dry season is surprising and similar dry season sinks have not to our knowledge been reported from other similar savanna ecosystems and could have potential management implications for agroforestry. A strong response of NEE versus small changes in plant available soil water content was found. Collection and analysis of flux data for several consecutive years including variations in precipitation, available soil moisture and labile soil carbon are needed for understanding the year to year variation of the carbon budget of this grass land/sparse savanna site in semi arid Sudan.</p

Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible